Year: 2025
Type: article
Abstract: Crossfire attacks disrupt network services by targeting critical links of server groups, causing traffic congestion and server failures that prevent legitimate users from accessing services. To counter this threat, this study proposes a novel topology spoofing defense mechanism based on a sequence-based Graph Neural Network–Moving Target Defense (ENRNN-MTD). During the reconnaissance phase, the method employs a GNN to generate multiple random and diverse virtual topologies, which are mapped to various external hosts. This obscures the real internal network structure and complicates the attacker’s ability to accurately identify it. In the attack phase, an IP random-hopping mechanism using a chaotic sequence is introduced to conceal node information and increase the cost of launching attacks, thereby enhancing the protection of critical services. Experimental results demonstrate that, compared to existing defense mechanisms, the proposed approach exhibits significant advantages in terms of deception topology randomness, defensive effectiveness, and system load management.
Source: Applied Sciences
DOI: 10.3390/app152111432
Authors: Dexian Chang, Xiaobing Zhang, Jiajia Sun, Chen Fang
Institution: PLA Information Engineering University
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
Award: G3689244366
OpenAlex ID: W4415560235